As tables REF  and REF  show, the GANformer2 model outperforms prior work along most metrics and datasets in both the conditional and unconditional settings. It achieves substantial gains in terms of FID score, which correlates with visual quality [1]}, as is likewise reflected through the good Precision scores. Notable also are the large improvements along Recall, in the unconditional and especially the conditional settings, which indicate wider coverage of the natural image distribution. We note that the performance gains are highest for the CLEVR [2]} and the COCO [3]} datasets, both focusing on varied arrangements of multiple objects and high structural variance. These results serve as a strong evidence for the particular aptitude of GANformer2 for compositional scenes.
